using caret package to find optimal parameters of GBM
gbm, optimization, r, r-caret
Solution
This link has a concrete example (page 10) - http://www.jstatsoft.org/v28/i05/paper
Basically, one should first create a grid of candidate values for hyper parameters (like n.trees, interaction.depth and shrinkage). Then call the generic train function as usual.
Problem
I'm using the R GBM package for boosting to do regression on some biological data of dimensions 10,000 X 932 and I want to know what are the best parameters settings for GBM package especially (n.trees, shrinkage, interaction.depth and n.minobsinnode) when I searched online I found that CARET package on R can find such parameter settings. However, I have difficulty on using the Caret package with GBM package, so I just want to know how to use caret to find the optimal combinations of the previously mentioned parameters ? I know this might seem very typical question, but I read the caret manual and still have difficulty in integrating caret with gbm, especially cause I'm very new to both of these packages